{
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  {
   "cell_type": "markdown",
   "id": "7db973a2-c95e-4939-a0d7-b54edec4d2cf",
   "metadata": {},
   "source": [
    "# Bitcoin Market Prediction uisng CoinmarketCap\n",
    "An AI-powered project using historical CoinMarketCap data to predict Bitcoin price trends and offer actionable insights for traders."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b792b517-bbc8-4e2c-bff2-45fad1a784dc",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51523d62-825a-4a15-aec2-7c910beb5fda",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import requests\n",
    "from dotenv import load_dotenv\n",
    "from bs4 import BeautifulSoup\n",
    "from IPython.display import Markdown, display\n",
    "from openai import OpenAI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e3816b0-4557-4225-bfb9-9933d813548a",
   "metadata": {},
   "source": [
    "## .env configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02be59e7-01cc-41b5-88c3-a47860570078",
   "metadata": {},
   "outputs": [],
   "source": [
    "load_dotenv(override=True)\n",
    "api_key = os.getenv('OPENAI_API_KEY')\n",
    "\n",
    "# Check the key\n",
    "\n",
    "if not api_key:\n",
    "    print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
    "elif not api_key.startswith(\"sk-proj-\"):\n",
    "    print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
    "elif api_key.strip() != api_key:\n",
    "    print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
    "else:\n",
    "    print(\"API key found and looks good so far!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fc32555-ea4e-45fe-ad44-9dbf4441afd1",
   "metadata": {},
   "source": [
    "### This line creates an authenticated OpenAI client instance, used to make API requests in your code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0845c687-6610-4f83-89e8-fb94bc47ddd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai import OpenAI\n",
    "openai = OpenAI(api_key=api_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d140db1a-dd72-4986-8f38-09f8d8f97b00",
   "metadata": {},
   "outputs": [],
   "source": [
    "headers = {\n",
    " \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
    "}\n",
    "\n",
    "class Website:\n",
    "\n",
    "    def __init__(self, url):\n",
    "        \"\"\"\n",
    "        Create this Website object from the given url using the BeautifulSoup library\n",
    "        \"\"\"\n",
    "        self.url = url\n",
    "        response = requests.get(url, headers=headers)\n",
    "        soup = BeautifulSoup(response.content, 'html.parser')\n",
    "        self.title = soup.title.string if soup.title else \"No title found\"\n",
    "        for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
    "            irrelevant.decompose()\n",
    "        self.text = soup.body.get_text(separator=\"\\n\", strip=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdc96768-94a8-4a08-acf1-32a62b699b94",
   "metadata": {},
   "outputs": [],
   "source": [
    "system_prompt = \"\"\"\n",
    "You are an intelligent assistant specialized in Bitcoin market prediction. Your tasks are:\n",
    "\n",
    "- Collect, preprocess, and analyze historical Bitcoin price and volume data sourced from CoinMarketCap historical data tables or API.\n",
    "- Extract relevant time series and technical features from OHLC (open, high, low, close) and volume data.\n",
    "- Use machine learning or statistical models to forecast future Bitcoin price trends.\n",
    "- Output clear, concise, and actionable insights, focusing on predicted price direction and potential trading signals.\n",
    "- Ensure all data collection respects CoinMarketCap’s terms of service.\n",
    "- Present findings in user-friendly language, explaining prediction confidence and market risks.\n",
    "- Continuously improve prediction accuracy through back-testing on updated datasets.\n",
    "\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d39e983-5b65-4de1-bdf0-e4239c3eb03f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def user_prompt_for(website):\n",
    "    user_prompt = f\"You are analyzing historical Bitcoin market data from the webpage titled '{website.title}'.\\n\"\n",
    "    user_prompt += (\n",
    "        \"The data includes daily open, high, low, close prices, trading volume, \"\n",
    "        \"and market capitalization presented in a table format.\\n\"\n",
    "        \"Please provide a clear and concise analysis in Markdown format, focusing on recent trends, \"\n",
    "        \"price movements, volatility, and any insights that could help forecast Bitcoin price directions.\\n\"\n",
    "        \"If possible, include technical indicators, significant patterns, or notable market events mentioned in the data.\\n\\n\"\n",
    "    )\n",
    "    user_prompt += website.text\n",
    "    return user_prompt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3d41ed3-4753-49f2-b51f-37e8be43102c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def messages_for(website):\n",
    "    return [\n",
    "        {\"role\": \"system\", \"content\": system_prompt},\n",
    "        {\"role\": \"user\", \"content\": user_prompt_for(website)}\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0eb99fcf-75a2-41b8-bf53-568f94264438",
   "metadata": {},
   "outputs": [],
   "source": [
    "# And now: call the OpenAI API. You will get very familiar with this!\n",
    "\n",
    "def summarize(url):\n",
    "    website = Website(url)\n",
    "    response = openai.chat.completions.create(\n",
    "        model = \"gpt-4o-mini\",\n",
    "        messages = messages_for(website)\n",
    "    )\n",
    "    return response.choices[0].message.content\n",
    "\n",
    "# A function to display this nicely in the Jupyter output, using markdown\n",
    "\n",
    "def display_summary(summary):    \n",
    "    display(Markdown(summary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0e57921-5132-40c6-834b-03a11a96425c",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://coinmarketcap.com/currencies/bitcoin/historical-data/3\"\n",
    "summary = summarize(url)\n",
    "display_summary(summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19d9b69a-6493-402d-a0b4-a486c322c816",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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